刺激驱动听觉注意的高密度脑电图数据集
A High-Density EEG Dataset for Stimulus-Driven Auditory Attention
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中文总结 AI 辅助
本研究提出一个神经生理学知情框架，整合刺激优先级与试验特定脑电图证据，用于无指令听觉竞争，通过门控决策级整合提升预测性能。
中文摘要 AI 辅助
刺激驱动的听觉注意决定了当多个声音源在没有明确聆听目标的情况下竞争时，哪个声音获得优先权，然而大多数计算研究要么侧重于声学显著性，要么侧重于解码预定义的目标。本研究使用刺激驱动的听觉注意（SAAD）范式研究无指令的听觉竞争，并开发了一个神经生理学知情的框架，该框架将刺激衍生的声音优先级与试验特定的脑电图证据相结合。使用Bradley-Terry模型进行的行为分析表明，从先前竞争估计的声音优先级可推广到未见过的声音配对，将留出预测的AUC从0.577提高到0.718。脑电图分析进一步揭示了与报告的选择侧相关的中晚期中央-颞叶偏侧化，神经信息在声学不对称之外仍具有预测性。基于这些发现，所提出的模型首先估计每个竞争声音的潜在优先级，并从它们的差异中形成相对刺激证据。一个多尺度脑电图通路，具有互补的有符号和基于功率的读出，然后提取试验特定的神经证据，通过门控决策级整合纳入。该框架使用镜像约束和配对留出协议进行评估，连同代表性的声学、脑电图、多模态基线和系统性消融。结果支持一种计算解释，其中自发听觉选择反映了可推广的刺激优先级与试验特定神经变异性之间的相互作用。
英文摘要
Stimulus-driven auditory attention determines which sound gains priority when multiple sources compete without an explicit listening goal, yet most computational studies focus either on acoustic salience or on decoding predefined attended targets. This study investigates instruction-free auditory competition using the Stimulus-driven Auditory Attention (SAAD) paradigm and develops a neurophysiologically informed framework that integrates stimulus-derived sound priority with trial-specific EEG evidence. Behavioral analysis using a Bradley--Terry model showed that sound priority estimated from previous competitions generalized to unseen sound pairings, improving held-out prediction from an AUC of 0.577 to 0.718. EEG analysis further revealed mid-to-late centro-temporal lateralization associated with the reported selection side, with neural information remaining predictive beyond acoustic asymmetry. Guided by these findings, the proposed model first estimates a latent priority for each competing sound and forms relative stimulus evidence from their difference. A multi-scale EEG pathway with complementary signed and power-based readouts then extracts trial-specific neural evidence, which is incorporated through gated decision-level integration. The framework is evaluated using mirror-constrained and pairing-held-out protocols, together with representative acoustic, EEG, multimodal baselines, and systematic ablations. The results support a computational account in which spontaneous auditory selection reflects the interaction between generalizable stimulus priority and trial-specific neural variability.
发表机构
- Systems Engineering Institute, School of Automation Science and Engineering, Xi’an Jiaotong University（西安交通大学自动化科学与工程学院系统工程研究所）
- Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
- Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算系）
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